As scientific data increasingly becomes a vital asset in research, innovation, and policymaking, the transition from siloed data management to collaborative governance has become a global necessity, particularly in complex, multi-stakeholder contexts like China. However, empirical understanding of what drives this transition remains limited. Using a refined Delphi method, this study identifies 27 key factors across six categories: external environment, stakeholders, infrastructure, governance organization, scientific data, and collaborative relationships. The findings reveal a multi-dimensional governance structure where institutional mechanisms and technological infrastructure provide the necessary foundation, while stakeholder incentives and trust-based relationships act as dynamic drivers. Specifically, the study highlights the intricate interplay among formal regulations, organizational structures, and informal relational dynamics. This study contributes to the theoretical discussion on scientific data governance and provides actionable insights for policymakers, data platform managers, and research institutions, aiming to overcome fragmentation and establish sustainable, collaborative data ecosystems.
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